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Model searching

Source: R/modelsearch.R
modelsearch.Rd

Performs Wald or score tests

Usage

modelsearch(x, k = 1, dir = "forward", type = "all", ...)

Arguments

x

lvmfit-object

k

Number of parameters to test simultaneously. For equivalence the number of additional associations to be added instead of rel.

dir

Direction to do model search. "forward" := add associations/arrows to model/graph (score tests), "backward" := remove associations/arrows from model/graph (wald test)

type

If equal to 'correlation' only consider score tests for covariance parameters. If equal to 'regression' go through direct effects only (default 'all' is to do both)

...

Additional arguments to be passed to the low level functions

Value

Matrix of test-statistics and p-values

See also

compare, equivalence

Author

Klaus K. Holst

Examples


m <- lvm();
regression(m) <- c(y1,y2,y3) ~ eta; latent(m) <- ~eta
regression(m) <- eta ~ x
m0 <- m; regression(m0) <- y2 ~ x
dd <- sim(m0,100)[,manifest(m0)]
e <- estimate(m,dd);
modelsearch(e,messages=0)
#>  Score: S P(S>s) Index  holm BH    
#>  0.04342  0.8349 y3~~x  1    0.8349
#>  0.04342  0.8349 y3~x   1    0.8349
#>  0.04342  0.8349 x~y3   1    0.8349
#>  0.04342  0.8349 y1~~y2 1    0.8349
#>  0.04342  0.8349 y1~y2  1    0.8349
#>  0.04342  0.8349 y2~y1  1    0.8349
#>  0.2946   0.5873 y1~~x  1    0.8349
#>  0.2946   0.5873 y1~x   1    0.8349
#>  0.2946   0.5873 x~y1   1    0.8349
#>  0.2946   0.5873 y2~~y3 1    0.8349
#>  0.2946   0.5873 y2~y3  1    0.8349
#>  0.2946   0.5873 y3~y2  1    0.8349
#>  0.7496   0.3866 y2~~x  1    0.8349
#>  0.7496   0.3866 y2~x   1    0.8349
#>  0.7496   0.3866 x~y2   1    0.8349
#>  0.7496   0.3866 y1~~y3 1    0.8349
#>  0.7496   0.3866 y1~y3  1    0.8349
#>  0.7496   0.3866 y3~y1  1    0.8349
modelsearch(e,messages=0,type="cor")
#>  Score: S P(S>s) Index  holm BH    
#>  0.04342  0.8349 y3~~x  1    0.8349
#>  0.04342  0.8349 y1~~y2 1    0.8349
#>  0.2946   0.5873 y1~~x  1    0.8349
#>  0.2946   0.5873 y2~~y3 1    0.8349
#>  0.7496   0.3866 y2~~x  1    0.8349
#>  0.7496   0.3866 y1~~y3 1    0.8349